BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

197 related articles for article (PubMed ID: 36006428)

  • 1. Utility of pre-treatment FDG PET/CT-derived machine learning models for outcome prediction in classical Hodgkin lymphoma.
    Frood R; Clark M; Burton C; Tsoumpas C; Frangi AF; Gleeson F; Patel C; Scarsbrook A
    Eur Radiol; 2022 Oct; 32(10):7237-7247. PubMed ID: 36006428
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Radiomics based on
    Ou X; Zhang J; Wang J; Pang F; Wang Y; Wei X; Ma X
    Cancer Med; 2020 Jan; 9(2):496-506. PubMed ID: 31769230
    [TBL] [Abstract][Full Text] [Related]  

  • 3. The Usefulness of Machine Learning-Based Evaluation of Clinical and Pretreatment [
    Nakajo M; Kawaji K; Nagano H; Jinguji M; Mukai A; Kawabata H; Tani A; Hirahara D; Yamashita M; Yoshiura T
    Mol Imaging Biol; 2023 Apr; 25(2):303-313. PubMed ID: 35864282
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Machine learning-based FDG PET-CT radiomics for outcome prediction in larynx and hypopharynx squamous cell carcinoma.
    Zhong J; Frood R; Brown P; Nelstrop H; Prestwich R; McDermott G; Currie S; Vaidyanathan S; Scarsbrook AF
    Clin Radiol; 2021 Jan; 76(1):78.e9-78.e17. PubMed ID: 33036778
    [TBL] [Abstract][Full Text] [Related]  

  • 5. The Impact of Semiautomatic Segmentation Methods on Metabolic Tumor Volume, Intensity, and Dissemination Radiomics in
    Driessen J; Zwezerijnen GJC; Schöder H; Drees EEE; Kersten MJ; Moskowitz AJ; Moskowitz CH; Eertink JJ; Vet HCW; Hoekstra OS; Zijlstra JM; Boellaard R
    J Nucl Med; 2022 Sep; 63(9):1424-1430. PubMed ID: 34992152
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Application of a Machine Learning Approach for the Analysis of Clinical and Radiomic Features of Pretreatment [
    Nakajo M; Jinguji M; Tani A; Kikuno H; Hirahara D; Togami S; Kobayashi H; Yoshiura T
    Mol Imaging Biol; 2021 Oct; 23(5):756-765. PubMed ID: 33763816
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Training and external validation of pre-treatment FDG PET-CT-based models for outcome prediction in anal squamous cell carcinoma.
    Frood R; Mercer J; Brown P; Appelt A; Mistry H; Kochhar R; Scarsbrook A
    Eur Radiol; 2024 May; 34(5):3194-3204. PubMed ID: 37924344
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Machine learning based evaluation of clinical and pretreatment
    Nakajo M; Jinguji M; Tani A; Yano E; Hoo CK; Hirahara D; Togami S; Kobayashi H; Yoshiura T
    Abdom Radiol (NY); 2022 Feb; 47(2):838-847. PubMed ID: 34821963
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Hodgkin disease: diagnostic value of FDG PET/CT after first-line therapy--is biopsy of FDG-avid lesions still needed?
    Schaefer NG; Taverna C; Strobel K; Wastl C; Kurrer M; Hany TF
    Radiology; 2007 Jul; 244(1):257-62. PubMed ID: 17581905
    [TBL] [Abstract][Full Text] [Related]  

  • 10. [
    Ferreira M; Lovinfosse P; Hermesse J; Decuypere M; Rousseau C; Lucia F; Schick U; Reinhold C; Robin P; Hatt M; Visvikis D; Bernard C; Leijenaar RTH; Kridelka F; Lambin P; Meyer PE; Hustinx R
    Eur J Nucl Med Mol Imaging; 2021 Oct; 48(11):3432-3443. PubMed ID: 33772334
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Value of [
    Li K; Sun H; Lu Z; Xin J; Zhang L; Guo Y; Guo Q
    Eur J Radiol; 2018 Sep; 106():160-166. PubMed ID: 30150039
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Prediction of local recurrence and distant metastasis using radiomics analysis of pretreatment nasopharyngeal [18F]FDG PET/CT images.
    Peng L; Hong X; Yuan Q; Lu L; Wang Q; Chen W
    Ann Nucl Med; 2021 Apr; 35(4):458-468. PubMed ID: 33543393
    [TBL] [Abstract][Full Text] [Related]  

  • 13. The usefulness of machine-learning-based evaluation of clinical and pretreatment
    Nakajo M; Nagano H; Jinguji M; Kamimura Y; Masuda K; Takumi K; Tani A; Hirahara D; Kariya K; Yamashita M; Yoshiura T
    Br J Radiol; 2023 Sep; 96(1149):20220772. PubMed ID: 37393538
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Diagnostic performance of 18F-2-fluoro-2-deoxy-D-glucose PET/computerized tomography in identifying bone marrow infiltration in new patients with diffuse large B-cell lymphoma and Hodgkin lymphoma.
    Kandeel AA; Hussein M; Zidan L; Younis J; Edesa W; Alsayed Y
    Nucl Med Commun; 2020 Mar; 41(3):269-279. PubMed ID: 31895758
    [TBL] [Abstract][Full Text] [Related]  

  • 15. The value of (18)F-fluorodeoxyglucose positron emission tomography/computed tomography for staging of primary extranodal head and neck lymphomas.
    Schrepfer T; Haerle SK; Strobel K; Schaefer N; Hälg RA; Huber GF
    Laryngoscope; 2010 May; 120(5):937-44. PubMed ID: 20422687
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Machine learning in the differentiation of follicular lymphoma from diffuse large B-cell lymphoma with radiomic [
    de Jesus FM; Yin Y; Mantzorou-Kyriaki E; Kahle XU; de Haas RJ; Yakar D; Glaudemans AWJM; Noordzij W; Kwee TC; Nijland M
    Eur J Nucl Med Mol Imaging; 2022 Apr; 49(5):1535-1543. PubMed ID: 34850248
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Baseline
    Aide N; Fruchart C; Nganoa C; Gac AC; Lasnon C
    Eur Radiol; 2020 Aug; 30(8):4623-4632. PubMed ID: 32248365
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Intratumor Heterogeneity Assessed by
    Lue KH; Wu YF; Liu SH; Hsieh TC; Chuang KS; Lin HH; Chen YH
    Acad Radiol; 2020 Aug; 27(8):e183-e192. PubMed ID: 31761665
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Prognostic significance of FDG-PET in relapsed or refractory classical Hodgkin lymphoma treated with standard salvage chemotherapy and autologous stem cell transplantation.
    Smeltzer JP; Cashen AF; Zhang Q; Homb A; Dehdashti F; Abboud CN; Dipersio JF; Stockerl-Goldstein KE; Uy GL; Vij R; Westervelt P; Bartlett NL; Fehniger TA
    Biol Blood Marrow Transplant; 2011 Nov; 17(11):1646-52. PubMed ID: 21601641
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Combined clinical and specific positron emission tomography/computed tomography-based radiomic features and machine-learning model in prediction of thymoma risk groups.
    Ozkan E; Orhan K; Soydal C; Kahya Y; Seckin Tunc S; Celik O; Dizbay Sak S; Kayi Cangir A
    Nucl Med Commun; 2022 May; 43(5):529-539. PubMed ID: 35234213
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.